1. 目的：SQL语句的Pandas实现
数据分析师最基础的技能之一：写SQL统计数据；
然..不够使，又须多一个能力，
数据分析师最基础的技能之一：Pandas数据处理
2. 环境
- python2.7
- python须安装ipython, pandas, numpy, mysqldb
- mysql, 并下载练习包employees database
如果不想花时间搭建环境, 我已经在云端搭建了数据学习环境，按照下面的文章最后的说明，申请远程访问权限:
2019年，数据民工的大白话规划
2.1 练习环境
- ipython
import MySQLdb
import pandas as pd
conn = MySQLdb.connect(host='127.0.0.1', user='usertest', passwd='usertest',port=3306, db='employees', charset="utf8")
cur = conn.cursor()
df_employees = pd.read_sql('select * from employees', con=conn)
- 备注:
python MySQLdb连接数据库，为防止查询结果中文乱码，connect参数注意增加charset=”utf8″(因为一般表创建字符charset为utf8)
3. SQL实例
下面开始一步一步对照SQL的统计语句和Pandas实现方法：
3.1 单表查询
3.1.1 查询前几行
- sql
select * from emloyees limit 10
- pandas
# 方法一：
df_employees.iloc[0:10]
# 方法二：
df_employees.head(10)
3.1.2 查询某个字段
- sql
select emp_no, birth_date from emloyees
- pandas
df_employees[['emp_no', 'birth_date']]
3.1.3 查询某字段前几行
- sql
select emp_no, birth_date from emloyees limit 10
- pandas
df_employees[['emp_no', 'birth_date']].iloc[0:10]
df_employees[['emp_no', 'birth_date']].head(10)
3.1.4 单字段筛选特殊值
- sql
select * from emloyees where hire_date='1986-06-26'
select * from emloyees where hire_date <> '1986-06-26'
- pandas
df_employees.loc[df_employees['hire_date'].astype(str)=='1986-06-26']
df_employees.loc[df_employees['hire_date'].astype(str) != '1986-06-26']
df_employees.loc[df_employees['hire_date'].astype(str) <> '1986-06-26']
3.1.5 and 和 or
- sql
select * from employees where hire_date='1986-06-26' and gender='F'
select * from employees where hire_date='1986-06-26' or gender='F'
- pandas 方法一
df_employees.loc[(df_employees['hire_date'].astype(str)=='1986-06-26') & (df_employees['gender'].astype(str)=='M')]
df_employees.loc[(df_employees['hire_date'].astype(str)=='1986-06-26') | (df_employees['gender'].astype(str)=='M')]
- pandas 方法二
df1 = df_employees.loc[df_employees['hire_date'].astype(str)=='1986-06-26']
df2 = df1.loc[df1['gender'].astype(str)=='F']
print df2
3.1.6 is null 和 is not null
- sql
select * from employees where first_name is null
- pandas
# 当然数据可能没有空值，所以结果为空
df_employees[df_employees['first_name'].isnull()]
df_employees[df_employees['first_name'].notnull()].head(10)
3.1.6 in操作
- sql
select * from employees where first_name in ('Parto', 'Anneke', 'Saniya')
- pandas
df_employees.loc[df_employees['first_name'].isin(['Parto', 'Anneke', 'Saniya'])]
3.1.7 not in操作
- sql
select * from employees where first_name not in ('Parto', 'Anneke', 'Saniya')
- pandas
df_employees.loc[~df_employees['first_name'].isin(['Parto', 'Anneke', 'Saniya'])]
# 希望通过某列转化的list作为in的输出参数
df_employees.loc[~df_employees['first_name'].isin(df.colexample.tolist())]
3.1.8 聚合操作：count()
- sql
select first_name, count(*) as tt from employees group by first_name order by tt
- pandas
df_employees.groupby('first_name').size()
# 注意比较size()与count()的区别，count()把每个字段都汇总，所以用size()即可
df_employees.groupby('first_name').count()
3.1.9 order by
- sql
select emp_no from employees order by emp_no desc
- pandas
df_employees.sort_values(by='emp_no', ascending=False)
3.1.10 聚合操作：count()和count(distinct)
- sql
select first_name, count(*) as tt from employees group by first_name order by tt
select gender,count(DISTINCT first_name) from employees group by gender
- pandas
df_employees.groupby('gender').count().sort_values('first_name', ascending=False)
df_employees.groupby('gender').first_name.nunique()
3.1.11 聚合操作：sum()
- sql
select gender, sum(emp_no) as tt from employees group by gender order by tt
- pandas
df_employees.[['emp_no', 'gender']].groupby('gender').sum()
# pandas对非int字段，不会进行sum操作，如下结果与上面代码运行结果一样：
df_employees.groupby('gender').sum()
3.1.12 多种聚合操作：count()与sum()
- sql
select gender, sum(emp_no) as tt1, count(*) as tt2 from employees group by gender order by tt1 desc
select gender,first_name, sum(emp_no) as tt1, count(*) as tt2
from employees group by gender,first_name
- pandas
df_employees.groupby('gender').agg({'emp_no':np.sum, 'first_name':np.size}).sort_values(by='emp_no', ascending=False)
df_employees.groupby(['gender','first_name']).agg({'emp_no':[np.sum, np.size]})
3.1.13 待更新
3.2 单表查询
3.2.1 连接：Left join，Rigth join，Full join
- 新读入薪水表
df_salaries = pd.read_sql('select * from salaries', con=conn)
- sql
select * from employees t1 left join salaries t2 on t1.emp_no=t2.emp_no
select * from employees t1 right join salaries t2 on t1.emp_no=t2.emp_no
select * from employees t1 full join salaries t2 on t1.emp_no=t2.emp_no
- pandas
pd.merge(df_employees, df_salaries , on='emp_no', how='left')
pd.merge(df_employees, df_salaries , on='emp_no', how='right')
pd.merge(df_employees, df_salaries , on='emp_no', how='outer')
# 如果是多列作为连接条件:
pd.merge(df_employees, df_salaries , left_on=['emp_no', 'hire_date'], right_on=['emp_no', 'to_date'], how='inner')
- join 函数
# 如果两个dataframe待连接字段名字不同
# 如下: 通过df_employees的columnName 与 df_salaries的newColumn进行连接
df_employees.set_index('columnName').join(df_salaries.set_index('newColumn'))
df_employees.join(df_salaries.set_index("birth_date"), on='hire_date', lsuffix='_one', rsuffix='_another', how='left')
3.2.2 union all
- sql
(select * from employees t1 limit 10)
union all
(select * from employees t1 limit 20,10)
- pandas
pd.concat([df_employees.iloc[0:10], df_employees.iloc[10:21]])
3.2.2 union all 和 union
- sql
(select * from employees t1 limit 10)
union all
(select * from employees t1 limit 20,10)
- pandas
pd.concat([df_employees.iloc[0:10], df_employees.iloc[10:21]])
pd.concat([df_employees.iloc[0:10], df_employees.iloc[10:21]])
3.2.3 row_number()
- sql
# mysql没有row_number()功能，oracle有, 如下模拟oracle写法(实际mysql无法运行哈，意会下精神，咳咳)
SELECT * FROM
( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY gender ORDER BY emp_no DESC) AS rn FROM employees t)
WHERE rn < 5
ORDER BY gender ,rn;
- pandas
(df_employees.assign(rn=df_employees.sort_values(['emp_no'], ascending=False).groupby(['gender']).cumcount()+1).query('rn<5').sort_values(['gender','rn']))
3.2.4 update
- sql
update employees
set emp_no=100
where emp_no=10001
- pandas
df_employees.loc[df_employees['emp_no']==10001, 'emp_no'] = 99
3.2.5 delete
- sql
delete employees
where emp_no=10003
- pandas
df1 = df_employees.loc[df_employees['emp_no'] != 10003]
待更新..
